Transcription of Using Random Forest to Learn Imbalanced Data
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Using Random Forest to Learn Imbalanced Data
Using Random Forest to Learn Imbalanced DataChao of Statistics,UC BerkeleyAndy Research,Merck Research LabsLeo of Statistics,UC BerkeleyAbstractIn this paper we propose two ways to deal with the Imbalanced data classification problem usingrandom Forest . One is based on cost sensitive learning, and the other is based on a sampling metrics such as precision and recall, false positive rate and false negative rate,F-measureand weighted accuracy are computed. Both methods are shown to improve the prediction accuracy ofthe minority class, and have favorable performance compared to the existing IntroductionMany practical classification problems areimbalanced; , at least one of the classes constitutes only avery small minority of the data.
Ling & Li (1998) over-sample the minority class by replicating the minority samples so that they attain the same size as the majority class. Over-sampling does not increase information; however by replication it raises the weight of the minority samples. Chawla et al. (2002) combine over-sampling and down-sampling
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